Actions364
- Continuous Activity Actions
- Dataset Actions
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- Compute Metrics
- Create Dataset
- Create Managed Dataset
- Delete Data
- Delete Dataset
- Execute Tables Import
- Get Column Lineage
- Get Data
- Get Data - Alternative Version
- Get Dataset Settings
- Get Full Info
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Dashboard Actions
- Data Collection Actions
- Data Quality Actions
- Compute Rules on Specific Partition
- Create Data Quality Rules Configuration
- Delete Rule
- Get Data Quality Project Current Status
- Get Data Quality Project Timeline
- Get Data Quality Rules Configuration
- Get Dataset Current Status
- Get Dataset Current Status per Partition
- Get Last Outcome on Specific Partition
- Get Last Rule Results
- Get Rule History
- Update Rule Configuration
- DSS Administration Actions
- Job Actions
- Library Actions
- Dataset Statistic Actions
- Discussion Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- LLM Mesh Actions
- Machine Learning - Lab Actions
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Adjust Forecasting Parameters and Algorithm
- Compute Partial Dependencies of Trained Model
- Compute Subpopulation Analysis of Trained Model
- Create ML Task
- Create Visual Analysis
- Create Visual Analysis and ML Task
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get ML Task Settings
- Get ML Task Status
- Get Model Snippet
- Get Partial Dependencies of Trained Model
- Get Scoring Jar of Trained Model
- Get Scoring PMML of Trained Model
- Get Subpopulation Analysis of Trained Model
- Get Trained Model Details
- Get Visual Analysis
- List ML Tasks of Project
- List ML Tasks of Visual Analyses
- List Visual Analyses
- Reguess ML Task
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Compute Subpopulation Analysis of Version
- Create Saved Model
- Delete Version
- Download Model Documentation of Version
- Evaluate MLflow Model Version
- Generate Model Documentation From Custom Template
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get MLflow Model Version Metadata
- Get Partial Dependencies of Version
- Get Saved Model
- Get Subpopulation Analysis of Version
- Get Version Details
- Get Version Scoring Jar
- Set Version User Meta
- Update Saved Model
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Macro Actions
- Plugin Actions
- Download Plugin
- Fetch From Git Remote
- Get File Detail From Plugin
- Get Git Remote Info
- Get Plugin Settings
- Install Plugin From Git
- Install Plugin From Store
- List Files in Plugin
- List Git Branches
- List Plugin Usages
- Move File or Folder in Plugin
- Add Folder to Plugin
- Create Development Plugin
- Create Plugin Code Env
- Delete File From Plugin
- Delete Git Remote Info
- Delete Plugin
- Download File From Plugin
- Move Plugin to Dev Environment
- Pull From Git Remote
- Push to Git Remote
- Rename File or Folder in Plugin
- Reset to Local Head State
- Reset to Remote Head State
- Set Git Remote Info
- Set Plugin Settings
- Update Plugin Code Env
- Update Plugin From Git
- Update Plugin From Store
- Update Plugin From Zip Archive
- Upload File to Plugin
- Upload Plugin
- Project Deployer Actions
- Get Deployment Settings
- Get Deployment Status
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- SQL Query Actions
- Wiki Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Project Actions
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- Streaming Endpoint Actions
- Webapp Actions
- Workspace Actions
Overview
The node integrates with the Dataiku DSS API to perform various operations on Dataiku resources. Specifically, for the Dataset resource and the Compute Metrics operation, it allows users to compute metrics defined on a specified dataset within a project. This is useful for data quality monitoring, analytics, and reporting workflows where metric computation on datasets is required.
Typical use cases include:
- Triggering metric computations on datasets after data ingestion or transformation.
- Automating data quality checks by computing relevant metrics programmatically.
- Integrating Dataiku DSS dataset metrics into broader automation or orchestration pipelines.
Example: A user can configure this node to compute all metrics on a dataset named "sales_data" in project "my_project" to update dashboards or trigger alerts based on metric results.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku project containing the dataset. |
| Dataset Name | The name of the dataset on which to compute metrics. |
| Query Parameters | Optional additional query parameters as key-value pairs to customize the API request. |
| Request Body | JSON object representing the body of the request; used for passing additional options. |
Output
The output is a JSON array where each item corresponds to the response from the Dataiku DSS API for the compute metrics action. The structure depends on the API response but generally includes computed metric values and related metadata for the dataset.
If the API returns binary data (not typical for this operation), it would be provided as binary output, but for Compute Metrics, the output is JSON.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials (an API key) for authentication.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API.
- No additional external services are needed beyond the Dataiku DSS API.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing credentials, ensure that the Dataiku DSS API credentials (API key and server URL) are configured correctly in n8n.
- Project Key or Dataset Name Required: The node requires both the project key and dataset name to be set for this operation. Missing these will cause errors.
- API Errors: Errors returned from the Dataiku DSS API (e.g., 404 if dataset not found, 403 for permission issues) will be surfaced as node errors. Verify project access and dataset existence.
- Invalid JSON in Request Body: If using the Request Body property, ensure the JSON is well-formed to avoid parsing errors.
- Network Issues: Connectivity problems to the Dataiku DSS server will cause request failures.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Datasets API Reference
- n8n Documentation on HTTP Request Node (for understanding underlying request mechanics)
This summary focuses on the Dataset resource's Compute Metrics operation as requested, describing its purpose, inputs, outputs, dependencies, and common troubleshooting points based on static analysis of the node code and provided properties.